Method, system and apparatus for targeting an offer

ABSTRACT

A system, apparatus, means, computer code, and method may include receiving data indicative of information associated with a user, determining a value of a metric associated with the user based on the data indicative of information associated with the user, verifying that the value of the metric associated with the user is valid, selecting an offer from a plurality of offers where each of the offers has a score associated with the value of the metric, and providing data indicative of the selected offer.

FIELD

[0001] Embodiments generally relate to a method, system and apparatusfor targeting one or more offers.

BACKGROUND

[0002] Many systems and methods for providing advertisements are wellknown. Some of the systems create a more efficient advertisingenvironment by providing ads to those individuals that are most likelyto be interested in the advertised product or service. Typical adtargeting is accomplished by analyzing data from previous ad campaignsto develop models to “target” where ads in a current or new campaignshould, or will be sent. Offers are both legally and practicallydifferent from advertisements however, and yet have for most purposesbeen treated similarly. Unfortunately, standard targeted advertisingmethods, as applied to offers for the sale of goods or services, haveproven deficient in their ability to maximize sales volume and revenue.

SUMMARY

[0003] Embodiments provide an offer targeting apparatus, means, computerprogram code, system, and method. According to some embodiments, amethod of targeting one or more offers may include receiving dataindicative of information associated with a user, determining a value ofa metric associated with the user based on the data indicative ofinformation associated with the user, verifying that the value of themetric associated with the user is valid, selecting an offer from aplurality of offers where each of the offers has a score associated withthe value of the metric, and providing data indicative of the selectedoffer.

[0004] In some additional embodiments, a method may include receivingdata indicative of information associated with a user; determining avalue of a first metric associated with the user based on the dataindicative of information associated with the user; verifying the valueof the first metric associated with the user; determining a value of asecond metric based, at least in part, on the value of the first metric;selecting an offer from a plurality of offers based at least in part onthe value of the second metric, each of the plurality of offers having ascore associated with the value of the second metric; and providing dataindicative of the selected offer.

[0005] In some embodiments, a method of targeting one or more offers mayinclude generating for each of a plurality of values of a metricassociated with a user scores associated with a plurality of offers,receiving fulfillment data on at least one of the offers, and updatingthe scores based on the fulfillment data.

[0006] In further embodiments, a method of targeting one or more offersmay include receiving a first signal where the first signal includesdata indicative of information associated with a user, selecting basedon the data indicative of information associated with the user aplurality of offers for simultaneous display on a web page, andproviding a second signal where the second signal includes dataindicative of the plurality of offers.

[0007] In some other embodiments, a system, apparatus, article ofmanufacture, means, computer program code, etc., may implement one ormore of the methods described herein.

[0008] With these and other advantages and features of the inventionthat will become hereinafter apparent, the invention may be more clearlyunderstood by reference to the following detailed description of theinvention, the appended claims, and the drawings attached herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0009]FIG. 1 is a block diagram of a system in accordance with someembodiments;

[0010]FIG. 2 is a block diagram of a system in accordance with someembodiments;

[0011]FIG. 3 is a method in accordance with some embodiments;

[0012]FIG. 4 is a screen display diagram in accordance with someembodiments;

[0013]FIG. 5 is a method in accordance with some embodiments;

[0014]FIG. 6 is a method in accordance with some embodiments;

[0015]FIG. 7 is a block diagram of a system in accordance with someembodiments;

[0016]FIG. 8 is a block diagram of a system in accordance with someembodiments;

[0017]FIG. 9 is a block diagram of an apparatus and system in accordancewith some embodiments;

[0018]FIG. 10 is a block diagram of an apparatus in accordance with someembodiments;

[0019]FIG. 11 is a block diagram of an exemplary database table inaccordance with some embodiments; and

[0020]FIG. 12 is a method in accordance with some embodiments.

DESCRIPTION

[0021] Applicant has recognized that there is a market opportunity totarget one or more offers to a user in an efficient, effective, precise,and real-time manner. Applicant has further realized that a marketopportunity exists for an effective targeted offer scoring model capableof protecting a user's privacy. Applicant has also realized that thereis a need to further enhance the ability of advertisers and sellers totarget offers to a user in a tiered manner. The term “tiered”, as usedherein, generally refers to a system or method for selecting multipleoffers that can be displayed or otherwise provided to a user. Some ofthe offers may be displayed more prominently than other offers. In someembodiments, the offers are selected for simultaneous display on a webpage. Also according to some embodiments, multiple offers may beselected using multiple selection methods based on multiple selectioncriteria.

[0022] As used herein, the term “offers” may include legal offers and/ornon-legal offers. Offers may differ from advertisements in several ways.For example, many offers are “legal offers”. Legal offers may includeoffers that create binding contracts that are enforceable in equity andat law. Such an offer may be or include a specific proposal provided bya party to a user to enter into an agreement with a user, that whencoupled with an acceptance by the user, creates a contract between theparty and the user. For example, a car dealer may “offer” to sell aspecific vehicle for a specific price, to a particular consumer. Such anoffer, if properly accepted by the consumer, would create a bindingcontact between the car dealer and the consumer for the sale of the car.A non-legal offer may be or include an offer that does not meet thecriteria to be a legal offer.

[0023] Advertisements, on the other hand, may not contain pricing orsale information directed to a specific consumer. In the absence of sucha specification, the advertisement fails to create a binding contractbetween a seller and a buyer. For example, the same car dealer mayadvertise that the newest model of a popular car is available for sale.The advertisement may contain appealing graphics, sounds, and/or vehicleinformation, and may even provide a price. Because the advertisement isdirected to the consuming public (or a portion thereof) at largehowever, no legally binding offer has been made (i.e., the price term isnot binding).

[0024] In general, offers contain all terms necessary to effectuate asale or purchase while advertisements only provide information that mayinclude some of the terms required for an offer. Both may (and often do)include graphics, text, sounds, animations, links, speech, and smells(e.g., perfume advertisements). As used herein, the term “offer” mayinclude any communication, data transmission, signal, object, message,or device that conveys or presents information associated with the saleor lease of products and/or services. Offers may be as simple as alisting of a single price and a single product, or may include aplurality of terms, conditions, warranties, and/or guarantees for aplurality of products and/or services.

[0025] An example of an offering process may be exemplified by a usernavigating through the Internet. The user may, for example, connect tothe Internet from a personal computer (PC) or other user device (e.g.,personal digital assistant, telephone) that displays web pages through abrowser. The user can then travel to various web pages or web siteshosted by one or more web content providers. For example, the user mayvisit the web site of a book seller. The book seller can provide variousforms of content on the web site, including book reviews, articles,stories, and an online store. An advertiser may wish to display offersto the user when the user visits the online store of the book seller.The advertiser may be, for example, a bicycle retailer or theadvertising representative of a bicycle retailer. The bicycle retailermay wish to send offers to a user who purchases or expresses interest inbooks relating to bicycles and/or bike riding or meets other targetingcriteria established by or for the bicycle retailer. For example, anoffer to such a user may include a special price or discount on abicycle, such as “new mountain bike for only $99”. Such an offer may besent directly to the book seller/content provider for display on thebook store web page, or may be selected and/or transmitted from an offertargeting device.

[0026] The offer targeting device may be a computer or computer serverthat is dedicated to serving offers to various content provider websites across the Internet. As used herein, the terms “serving” or“serving offers” generally refer to the displaying, showing,downloading, providing, distributing, and/or transmitting of offers, webpages, or other materials to various viewers, buyers, consumers, andother users. The offer targeting device may be operated by, or on behalfof the advertiser, or may be a third-party entity that sits between theadvertiser and the content provider. Information about the user may beused by any of the content provider, the advertiser, and the offertargeting device to serve or display personalized or targeted offers tothe user.

[0027] Some embodiments described herein provide a method of targetingone or more offers in which user information is verified in order tomore accurately select one or more offers that may be provided to auser, and/or more appropriately charge advertisers for the offersproperly shown. In some embodiments, the user information may bereceived from the user (e.g., during a user registration process), froma content provider (e.g., a content provider that is hosting a web siteaccessed by the user), from a database (e.g., a database storingpreviously obtained user information), or from various other entitiesand/or sources. Some embodiments contemplate real-time and other dynamicupdating of a scoring model to enhance offer selection for targetingoffers to a user. Some embodiments also provide for a tiered offertargeting scheme which increases the probability that a user will takeaction in relation to an offer or plurality of offers. Otherconfigurations and benefits will become apparent to those skilled in theart from the descriptions, illustrations, and examples herein.

[0028] Referring first to FIG. 1, a block diagram of a system fortargeting offers 100 is depicted for use in explanation, but notlimitation, of described embodiments. Upon reading this disclosure,those skilled in the art will appreciate that different types, shapes,and configurations of systems, apparatus, and methods may be used.

[0029] In some embodiments, the system for targeting one or more offers100 may include an offer server 102 in communication with an advertisingdevice 104 and a content providing device 106. In some embodiments, thecontent providing device 106 hosts one or more web pages or web sitesthat may be accessed by one or more user devices 108 (e.g., a computer,a personal digital assistant, a cellular or wireless telephone, etc.)operated by various users. The content providing device 106 may providethe offer server 102 with information regarding a user that accesses thecontent provider's web site(s). Using this information, the offer server102 may select one or more offers provided by the advertising device 104and/or the content providing device 106, and provide the information orother data regarding the one or more offers to the content providingdevice 106. The content providing device 106 can then insert informationregarding the one or more offers into one or more web pages hosted bythe content providing device 106 and served to the user device 108 suchthat they are displayed to a user on the user device 108. These offersare “targeted” because they are provided to the specific user based oncharacteristics associated with the specific user and/or other targetingcriteria.

[0030] As used herein, the terms “communication” and “connection”generally refer to or may include, without limitation, any known oravailable method, device, or system which enables two or more devices tosend and receive signals, data, information, etc., between themselvesand/or other devices. Examples of communication types and/or connectionsinclude, but are not limited to, the World Wide Web, a Local AreaNetwork (LAN), a Metropolitan Area Network (MAN), a Wide Area Network(WAN), a proprietary network, a Public Switched Telephone Network(PSTN), a wireless network, and/or an Internet Protocol (IP) networksuch as the Internet, an intranet, or an extranet. Similarly, thesignals used in such communications may be radio frequency (RF),infrared radiation (IR), satellite, cellular, wireless, optical,microwave, or of any other known or available type and/or combination oftypes.

[0031] The offer server 102 may be any known or available type ofcomputing device including, but not limited to, a PC, a server, a webserver, host or mainframe computer, and any other type of device known,available, or described herein. The offer server 102 receives dataindicative of one or more offers from the advertising device 104. Theadvertising device 104 may be owned, controlled, monitored, or operatedby or on behalf of an advertiser, merchant, or other product sellerand/or service provider. In some embodiments, the targeted offer system100 may include more than one advertising device 104. Offers, which maybe directed to offering any number of possible goods and services, aretransmitted from the advertising device 104 to the offer server 102 foruse by a content providing device 106 in providing offers to users. Insome embodiments, the content providing device 106 may be or include anydevice, entity, site, or location in or on which an advertiser may wishto place offers. In some embodiments, the content providing device 106is a web publishing server. Also, according to some embodiments, theoffers may be provided to the content providing device 106 for ultimatetargeted placement and/or viewing on or by a user device 108. In thisway, a user operating a user device 108 may visit a web site hosted by acontent providing device 106 and view or receive targeted offers.

[0032] According to some embodiments, a user device 108 is incommunication with and/or connected to the content providing device 106.In some embodiments, the targeted offer system 100 may include more thanone user device. In some embodiments, the user device 108 may be a PC,laptop, personal digital assistant (PDA), or cellular, wireless, orwired telephone, or other wired or wireless device operated by or onbehalf of a user wishing to access the content providing site 106. Insome embodiments, the user device 108 is a PC operated by a user andconnected to the Internet. The user device 108 may be used to accessvarious content providing devices 106 connected to the Internet andoperated by various content providers. When a user device 108 isconnected to a content providing device 106 (e.g., accessing a web siteor web page hosted by the content providing device 106), the offerserver 102 may cause or facilitate information regarding one or moreoffers provided by the advertising device 104 to be sent to the contentproviding device 106, which in turn causes or facilitates informationregarding the one or more offers to be sent to the user device 108 fordisplay by the user device 108 (e.g., display on a web page served bythe content providing device 106 to the user device 108). It is in sucha manner that targeted offers may be disseminated from advertisingdevices 104 to user devices 108.

[0033] To assist with and generally enhance the offer targeting process,and according to some embodiments, an information device 110 isconnected to and/or in communication with the offer server 102. In someembodiments, the targeted offer system 100 may not include anyinformation devices 110 or may include more than one information device110. The information device 110 may be an on-line or off-lineinformation source. In some embodiments, the information device 110provides off-line demographic data to the offer server 102 so that theoffer server 102 may verify the validity of user data received fromeither a content providing device 106 or a user device 108. Theinformation device 110 may also be a device and/or database internal tothe offer server 102 and used to store, aggregate, and/or processdemographic or other information.

[0034] Those skilled in the art will appreciate that each of thetargeted offer system 100 and the devices 102-110 shown therein may beor include one or multiple computers, software applications, servers,programs, procedures, devices, or any portion, piece, component, orcombination thereof. Similarly, the targeted offer system 100 maycomprise fewer or more devices than those shown in FIG. 1. Multiples ofany device 102-110 may also be provided without deviating from the scopeand purpose of the claimed embodiments. The devices 102-110 may be indirect, indirect, continuous, intermittent, wired, or wirelessconnection and/or communication through any known or availablecommunications medium. Any and all devices 102-110 may also residewithin, adjacent to, or attached to any other device 102-110, and anydevice 102-110 may be connected to and/or in communication with anyother device or devices 102-110. For example, the offer server 102 maybe in direct communication with one or more user devices 108 so thatoffers may be sent directly from the offer server 102 to a user device108. In some embodiments described herein, the offer server 102 sendsoffers via e-mail directly to a user device 108.

[0035] Turning now to FIG. 2, a block diagram of a system for targetingoffers 100 according to some embodiments is shown. The targeted offeringsystem 100 includes the offer server 102, the advertising device 104,the content providing device 106, the user device 108, and theinformation device 110, previously described above. Also shown in FIG. 2are an information path 112, an offer path 114, and a fulfillment path116. According to some embodiments, the information path 112 is used bythe content providing device 106 to send user information or otherrelated data to the offer server 102. The types and purposes of suchinformation are described elsewhere herein. Offer path 114 may be usedto send offers or other offer-related data or information from the offerserver 102 to the content providing device 106. The fulfillment path116, according to some embodiments, may be used to send offer responseand/or offer fulfillment data or related information from the contentproviding device 106 to the offer server 102. Each path described herein112-116 may be any known or available internal or external connection,port, bus, channel, wire, device, or system that enables unidirectionalor multidirectional communication. Any one or more of the describedpaths 112-116 may be or represent the same physical entity, channel,network, connection or device, or any component or portion thereof. Insome embodiments, any one or more of the paths 112-116 may includemultiple paths. The targeted offer system 100 may also include more orfewer paths than described herein without deviating from the scope andpurpose of the claimed embodiments.

[0036] Turning now to FIG. 3, a method of targeting offers 118,according to some embodiments is shown. At 120 the offer server 102receives data indicative of information associated with a user. As usedherein, the phrase “data indicative of” refers to data, informationand/or signals that represent, indicate, and/or describe a particularitem, device, person, value, or idea. The data may also be associatedwith or related to the particular item, or may indeed be the itemitself. For example, the data received by the offer server 102 at 120may be, in some embodiments, information associated with a user. Theinformation associated with a user may include any number of variables,metrics, values, or formulas associated with the user. Examples of suchinformation include, but are not limited to information regarding theuser's age, gender, marital status, employment, ethnicity, income,contact information (address, zip code, zip+4, phone number, e-mailaddress, etc.), purchase history, interests, and/or hobbies.

[0037] For example, data indicative of information associated with auser may be or include a value of a metric associated with the userand/or a code, variable, or indicator of such value. For instance, thedata may be or include a zip+4 code value of “210320129” indicating thezip+4 code in which the user resides or works. Thus, the zip+4 code isthe metric and “210320129” is the metric's value for the specific user.In some embodiments, the data may be or include a code such as “0000124”which corresponds to the stored zip+4 code value of “210320129”. Alsoaccording to some embodiments, for example, the data may be or include astreet address of “83 Low Street” indicating the street address wherethe user resides or works. The metric is the street address, and thevalue of the metric is “83 Low Street”. In some embodiments, the valueof the metric (e.g., “83 Low Street”) may be used to determine a valueof a second metric (i.e., a value of “029310293” for a zip+4 metricassociated with the user).

[0038] The data may be transferred from a user device 108 to a contentproviding device 106 in accordance with various interactions typicalbetween the devices (e.g., a user registration process), and thendirected through an information path 112 to the offer server 102. Thedata may also originate from a memory or storage device of the contentproviding device 106 (i.e., a previously registered user comes back tothe content site and associated user information data is loaded frommemory or retrieved from a database). In some embodiments, the data mayalso originate from an advertising device 104 (i.e., an advertiser mayhave user information from previous offer campaigns that it wishes toincorporate into the current targeted offer campaign). At 122 the datareceived by the offer server 102 is analyzed to determine at least onemetric and/or at least one value of a metric associated with the user.As described herein, the data indicative of information associated withthe user may include one or many metrics or values of a metricassociated with the user. For example, a metric may be a telephonenumber, and the value of the metric may be “(891) 023-9410”, indicatinga home, work, cellular, or other telephone number associated with theuser.

[0039] In some embodiments, information about or associated with a usermay have been obtained from the user in the past. In addition, a contentproviding device (e.g., a web site server) may have placed an electronic“cookie” on a user device when the user previously accessed the contentproviding device with the user device. The cookie may be associated witha code or other identifier that is associated with the user. When theuser accesses the content providing device again, the content providingdevice can retrieve the cookie from the user device and identity theuser based on the cookie. This may allow the content providing device toretrieve, access, request or otherwise obtain information about the userwithout the user again supplying the information. The content providingdevice can then provide some or all of this information to the offerserver 102 as part of 120. The content providing device also may providethe user code or other identifier to the offer server 102, which mayallow the offer server to identify the user and obtain additionalinformation regarding the user. This may occur particularly when theoffer server 102 is selecting and/or providing offers to one or morecontent providing devices, each of which may provide different userinformation to the offer server 102. In this way, the offer server 102can develop a profile of the user based on information provided from thedifferent content providing devices or other sources.

[0040] In some embodiments, the data may arrive from the contentproviding device 106 as or in a string or stream of information. During122, the string may be parsed examined, or otherwise used by the offerserver 102 to extract or obtain one or more metrics or one or morevalues of one or more metrics. The metric may be determined or usedbased on the type and/or number of metrics available in the data, or maybe chosen or used because of a known or suspected relevance orcorrelation to marketing, advertising, and/or offering statistics. Forexample, in some embodiments, the metric is a zip+4 code and the valueof the metric is the user's specific zip+4 code. There are approximatelythirty-three million zip+4 codes in the United States, each typicallyrepresenting one to six business and/or households. During 122 the offerserver 102 obtains the user's specific zip+4 code from the data itreceived or obtained during 120. In some embodiments, the value of themetric may be used to determine at least one other value of a metricassociated with the user. For example, the data received at 120 may be auser's street address and the zip+4 code corresponding to the user'sstreet address may be determined (e.g., looked-up in a table of allknown zip+4 codes and corresponding addresses) based on the value of thestreet address metric.

[0041] During 124, the offer server 102 and/or some other deviceconfirms or otherwise verifies the value of the metric determined during122. In some embodiments, verifying may not be needed and 124 might notbe performed or used. The metric may be verified by cross-referencingits value for a user with a set of known and/or stored values for themetric or the metric values for the user. In some embodiments, aninformation device 110 may be a third-party demographics provider thatcross-references the metric and verifies its accuracy and/or validity onbehalf of the offer server 102. The offer server 102 and/or one of itscomponents may also perform such verification either alone, or incombination with one or more information devices 110. For example, theoffer server 102 or an information device 110 of a demographics providermay attempt to locate a user's zip+4 code in a table of all known zip+4codes and verify it with regard to the user's street address, city,and/or state information.

[0042] For example, if a user enters a zip+4 code of “004630001” into acontent providing device 106, the content providing device 106 mayforward that information to the offer server 102 (i.e., for verificationand/or offer targeting). In some embodiments, the content providingdevice 106 may also transmit other information and/or metrics associatedwith the user, such as a street address or telephone number. Forpurposes of this example, assume that the street address of the user,“100 Main Drive”, is also sent to the offer server 102. The offer server102 may then look up the zip+4 code of “004630001” in a database of allknown zip+4 codes. The database may be maintained and/or housed by anyof various devices and in any manner known, available, and/or describedherein. Within the records associated with the zip+4 code “004630001”,the offer server 102 may search for the street address “100 Main Drive”.If “100 Main Drive” is a street address that corresponds to a recordassociated with the zip+4 code “004630001”, then the zip+4 code may bedeemed “valid”.

[0043] The terms “valid” and “validity” as used herein refer to one ormore values of a metric that have been matched and/or correlated to orwith other metric values associated with a user. According to someembodiments, a “valid” value of a metric may be a value that has adefinitive and/or higher probability of not being a false (e.g., madeup) or miss-entered (i.e., a typographical error). Thus, verifying avalue of a metric may be or include determining that the value has orexceeds a threshold likelihood or percentage of being correct. In someembodiments, another metric such as a telephone number and/or area codemay be used to further verify the value of the user metric (i.e., thestreet address). If the street address for the user cannot be located,then the information for the metric has either been entered erroneouslyor fraudulently. That is, the user's information is inaccurate.

[0044] For example, assume that the street address of the user, “100Main Drive”, is the value of the metric sent to the offer server 102.The offer server 102 may then look up the zip+4 code of “004630001” in adatabase of all known zip+4 codes and associated street addresses. Thedatabase may be maintained and/or housed by any of various devices andin any manner known, available, and/or described herein. Within therecords associated with the street address “100 Main Drive”, forexample, the offer server 102 may search for a zip+4 code. If a zip+codesuch as “004630001” corresponds to a record associated with the streetaddress “100 Main Drive”, then the street address may be deemed “valid”.In this manner, a first metric and/or value of a first metric (i.e., thestreet address) may be used to determine a second metric and/or thevalue of a second metric (i.e., the zip+4 code).

[0045] In the case of erroneously entered information, the offer server102, the information device 110, or a combination of the two may attemptto correct the information. Logical processes may be conducted, forexample, using other metrics associated with the user to determine theappropriate identity of the user and all necessary metric values.Because the value of the chosen metric is, for targeting purposes, an“identity” of the user, it will be referred to as such herein. It shouldbe noted that in some embodiments where non-personal identifiableinformation (“non-pii”) is chosen as a metric (e.g., zip+4 code), this“identity” does not identify a specific individual or user for purposesbeyond targeted offering. Thus, the user's privacy can be maintained.

[0046] If the metric cannot be corrected or verified however, than theuser is unidentifiable. Because the user's information is not validhowever, no informative targeting decision can be reached regarding theuser, because the identity of the user (in as much as the specificmetric chosen) is unknown. In some embodiments, a special type oftargeted offer may be appropriately directed to unidentifiable users.Marketing data may indicate, for example, that users entering false dataare likely to accept a certain type of offer. For example, users thatprovide false information may be likely to accept an offer for debtconsolidation. For example, even though the identity of the user is notinitially known or ascertainable, an offer appropriate for such a classof users (a pseudo-targeted or class-targeted offer) may nonethelessincrease the chances that a served offer will be accepted or acted upon.In some embodiments unidentified users may be served either a defaultoffer or no offer at all.

[0047] Once the value of the metric associated with the user has beendetermined and verified, the offer server 102 selects an offer from aplurality of offers at 126 based on the value of the metric. Informationregarding the offers may be stored within the offer server 102, withinthe content providing device 106, or may be received from an advertisingdevice 104. Each offer and/or offer type has a score associated witheach known or available value of the chosen metric. For example, eachoffer may have a rank ranging from one to ten for each and every zip+4code. Zip+4 codes where the offer performs or is likely to perform verypoorly (i.e., is not accepted or is not likely to be accepted) may havescores of one or two, while more preferable zip+4 codes where the offeris often accepted or is likely to be accepted may have scores of nine orten. In some embodiments, the offer that has the highest rank for theparticular zip+4 code metric value associated with the particular useris selected. In some embodiments, multiple offers are selected from theplurality of available offers. Examples of other score types, methods ofderiving them, and methods of selecting offers are described elsewhereherein. As used herein, the term “offer type” generally refers to atype, class, or group of offers having at least one characteristic incommon. For example, magazine offers are offers related to the purchaseof magazines and/or magazine subscriptions. Offers for particularmagazines and/or magazine subscriptions are members of the same offertype (i.e., magazine offers). The terms “offer type” and “offer class”may be used interchangeably herein.

[0048] In some embodiments, additional information, rules, heuristics,etc. may be used to select one or more offers during 126. For example,the offer server 102 may use a user's age, gender, personal preferences,user device type, IP (Internet Protocol) address, connection speed,location, income level, occupation, past offer acceptance or rejectioninformation, etc. to help it select one or more offers. As anotherexample, advertisers may have rules regarding when, how often, where, inwhat context, on what web sites, to whom, etc. their offers can beprovided or displayed which the offer server 102 may take into accountin some embodiments.

[0049] At 128 the offer server 102 provides, via an offer path 114, dataindicative of the selected offer or offers to the content providingserver 106. In some embodiments, data indicative of the selected offermay be a variable, an integer, a flag, a code, or any other indicator ofthe offer, and/or information (i.e., text, graphics) regarding theselected offer itself. For example, the data may include a code such as“001” which is associated with a selected offer, or may contain text,graphics, sounds, and/or other data associated with or defining theselected offer. According to some embodiments, the data indicative ofthe selected offer or offers may be received from another device such asa content providing device 106. In some embodiments, informationregarding the plurality of offers is stored on the content providingdevice 106. In such embodiments the offer server 102 may provide anoffer code or a Boolean operator to the content providing device 106.The offer code represents an offer amongst the plurality of offerslocated on the content providing device 106 that the offer server 102has selected. By receiving the offer code, the content providing device106 knows which offer the offer server 102 selected. Those skilled inthe art will recognize that various variables, integers, flags, codes,or indicators may be used to identify a selected offer. In someembodiments, the operator and/or owner of the content providing device106 may choose whether or not to send the offer selected by the offerserver 102 to a user device 108 for display. The offer server 102 thusacts as an “offer targeting consultant” or “offer selection consultant”for the content providing device 106. In other embodiments the offercorresponding to the offer code provided by the offer server 102 may beautomatically sent by the content providing device 106 to a user device108 for display.

[0050] A Boolean operator or other indicator supplied to the contentproviding device 106 may indicate a yes/no decision regarding apre-determined offer. For example, a single “bill me later” offer may beavailable for display by or on a user device 108. A “bill me later” or“bill me” type offer is an offer that can be accepted by a user withoutrequiring payment of the agreed-upon offer price at the time of thetransaction. For example, a magazine “bill me” offer can be accepted bya user and the user may be billed at some later time (e.g., a bill maybe sent to the user along with the first magazine in the subscription).The offer server 102 provides the content providing server 106 with aBoolean operator or other indicator of “Yes” indicating that the currentuser should be shown the “bill me” offer, or “No”, indicating that thecurrent user should not be shown the “bill me” offer. Because a defaultoffer may also be pre-specified, the negative Boolean operator may alsoindicate that the default offer should be shown to the current user.Instead of a pre-specified single default offer, any number of othermultiple offer selection schemes may be used to handle the negativeBoolean operator situation. In some embodiments, the negative Booleanoperator may indicate to the content providing device 106 that no offershould be shown to the current user.

[0051] In embodiments where the information regarding the plurality ofoffers are stored and/or located on the offer server 102 or theadvertising device 104, the provided data indicative of the selectedoffer may be the offer itself (e.g., text, graphics, offer details). Forexample, the offer server 102 may send a selected offer or offers, viaan offer path 114, from either within the offer server 102 or theadvertising device 104 to the content providing device 106. In suchembodiments, the content providing device 106 is not required to storeinformation regarding a potentially large number of offers and relateddata, thus reducing the storage capacity needed of the content providingdevice 106. In some embodiments, the content providing device 106 mayincorporate a selected offer or offers sent from the offer server 102into a pre-existing or newly or dynamically created web page. Suchoffers may be inserted, for example, into a web page or web flowdisplayed on a user device 108 in association with a user registrationor login process.

[0052] According to some embodiments where the plurality of offers arestored and/or located outside of the content providing device 106, theoffer server 102 may serve a web page including the one or more selectedoffers. The web page may be created dynamically by the offer server 102,or may be derived from a template or other means of construction. Theoffer server 102 may send the web page over an offer path 114 to thecontent providing device 106 for incorporation into the web flow of thecontent providing device 106, or may serve the web page itself. Forexample, the offer server 102 may not only receive data indicative ofinformation associated with a user at 120, but may also have a userdirected to a web page or site of the offer server 102. In someembodiments, the provided data indicative of the selected offer at 120may include a uniform resource locator (URL) identifying a web page orsite of the offer server 102.

[0053] Once a user is directed to the URL of the offer server 102, a webpage may be displayed by the offer server 102 that includes the one ormore selected offers. In some embodiments it may be desired for theoffer server 102 to create or design the served web page to look similarto the web pages served by the content provider 106. The web page servedby the offer server 102 may be of various styles, types, or designs bothknown to those skilled in the art and described elsewhere herein. Insuch a manner the user may continue navigating through the web flowwithout indication that the flow migrated from the content providingdevice 106 to the offer server 102. The user device 108 may becomeeither directly connected to the offer server 102 or may be directed orconnected through the content providing device 106. After some action,interaction, or fulfillment of a pre-determined condition (e.g., aperiod of time), the user may be directed or sent back to the contentproviding device 106 for a continuation of the appropriate web flow.

[0054] In some embodiments, a web page served by the offer server 102 at128 may include a number of various controls. The controls may beActiveX® Controls or Java™ Applets such as checkboxes, pick-lists,forms, or any other known or available input mechanisms. The use ofcontrols allows user interactions to be monitored and/or recorded. Suchinteractions may be indicative of interest in or acceptance of one ormore of the selected and/or displayed offers. In some embodiments, thisfulfillment or offer response data can be an important metric for use inunderstanding how different types of users are likely to react todifferent types and/or classes of offers. In some embodiments, offerresponse data may be or include data associated with offer impressions(i.e., data regarding how many times an offer has been shown to a user),data indicative of or otherwise associated with offer acceptances and/orrejections, data associated with user actions, data associated with userinaction, data associated with offer-related user purchases, and/orother data associated with users and their interactions with offers. Forexample, offer response data may include the number and/or sequence ofkeystrokes a user makes while viewing an offer, the mouse and/or pointermovements made by a user viewing an offer, the failure of a user tointeract with and/or accept and offer, the number of times an offer isshown to a user (or users), and/or the quantity, location, price, and/ortype of purchases made by a user (that may or may not be related to anoffer shown to the user).

[0055] In embodiments where the offer server 102 serves a web page to auser, this offer response data may be directly recorded by the offerserver 102 via an internal fulfillment path 116. Where the contentproviding device 106 serves the selected offer or offers, the offerresponse data may be transmitted from the content providing device 106through an external fulfillment path 116. In some embodiments,fulfillment data corresponding to an offer-related user purchase may bereceived over a fulfillment path 116 from either the content providingdevice 106 or a third-party vendor or merchant.

[0056] In some embodiments, offer response and/or fulfillment data maybe used, according to some embodiments, to both generate offer reportsand to update a scoring model. For example, offer response informationregarding one or more users' acceptance of rejection of an offer may beused to update a scoring model associated with the offer. As anotherexample, the offer server 102 may use offer acceptance and rejectioninformation for an offer provided to multiple content providing devicesto update a scoring model for the offer. Thus, information regardingacceptance or rejection of an offer by multiple users, display of theoffer by multiple content providing devices, etc. may be used to refinethe scoring model for the offer.

[0057] The data also may be used, for example, by the offer server 102to create a report describing the performance of certain offers and/orto generate a billing report associated with the offers. The report maybe sent by the offer server 102 to an advertising device 104. Theadvertising device 104 may utilize the report to assist in formulationof new offers, decide which offers it wishes to show or which offers toinclude in the plurality of stored offers, and/or to send appropriatepayment for offers selected by the offer server 102 and/or displayed onuser devices 108.

[0058] The offer server 102 may use offer response and fulfillment datato create and/or update a scoring model. The scoring model may be anyvarious method, formula, process, procedure, or combination thereofknown or available to those skilled in the art. In some embodiments thescoring model is a regression model directed to a particular metricassociated with a user. Whichever type (or types) of scoring model isemployed, the offer response and fulfillment data may be used to createand/or update the model and the scores associated with each of theplurality of offers. In some embodiments, because offer response and/orfulfillment data may be received and processed in real-time, the scoringmodel and respective offer scores may also be updated in real-time.Thus, according to some embodiments the selection of one or more offersat 126 may be updated to reflect the latest probabilities for each userin communication with the offer targeting system 100. This enhances theability of the offer targeting system 100 to increase the amount ofoffers that are accepted by users, and thus increases the costeffectiveness of an advertiser's marketing dollars. In some embodiments,the offer scores and/or the scoring model may be transmitted to acontent providing device 106, an advertising device 104, an informationdevice 110, or any other available device or combination thereof, foruse, processing, storage, or sale.

[0059] In some embodiments, the offer server 102 may use additionalinformation, data, or variables to assist in selecting one or moreoffers, refine or update scoring models, etc. For example, if the offerserver knows that a user is male, the offer server 102 may select one ormore primary and/or secondary offers that have a higher acceptance rateby males than females. The selection of these offers may be in additionto any offer(s) selected in accordance with 126 described above. Asfurther examples, an advertiser may want its offers displayed only oncertain web sites and/or at certain times or prohibit display of itsoffers on certain web sites and/or certain times. A web site may refuseoffers related to specific subject matter (e.g., a school may not wantoffers for guns displayed on its web site). An advertiser may not wantan offer for perfume to be display on a Web site devoted to big gamehunting or the same offer provided to a user within a certain time frameor if the user rejected the offer the first time. In addition,advertisers may want different versions of offers provided to a userdepending on the user's type of user device (e.g., cellular telephoneversus computer), connection speed (e.g., dial up connection versusbroadband connection), location (e.g., an offer may be tailoreddifferently for a person in California as opposed to a person in NorthCarolina), etc. As illustrated by these examples, the offer server 102may alter selection of one or more offers based on advertiser rules,user demographic profile, content provider rules, the relationship orlack of a relationship between an offer and content provided by acontent providing device (e.g., subject matter relationship),information obtained through use of a cookie, offer acceptance orrejection history, user history, a complementary relationship betweentwo offers (e.g., if one offer is accepted by a user, another offer hasan increased chance of being accepted by the user), a non-complementaryrelationship between two offers (e.g., if one offer is rejected by auser, another offer may have an increased chance of being accepted bythe user), a user's location, etc.

[0060] Referring now to FIG. 4, a representative screen display diagramfor targeting offers according to some embodiments is shown. In someembodiments, a user operating or associated with a user device 108 is incommunication with and/or connected to the targeted offer system 100.The user device 108 may include various components, some or all of whichmay be typical to computing devices such as a PC. In some embodiments,the user device 108 has a display screen 130 on which a browser 132operated by the user device 108 is displayed and used. The browser 132may be any known or available browser including Microsoft® Corporation'sInternet Explorer™. Included within the browser 132 may be a window,display, or representation of a URL 134 for facilitation of web browsingand surfing. Such URL 134 may indicate, for example, the Internetaddress of the current, requested, or previous web page accessed by theuser. The example page shown in FIG. 4 is a web page containing variousoffers for magazines and/or magazine subscriptions. At the top of theweb page are three primary offers 136, with a listing of multiplesecondary offers 138 being presented underneath. Further detailsregarding use of primary offers 136 and secondary offers 138 areprovided below. Additional discussion regarding FIG. 4 also is providedbelow.

[0061] Referring now to FIG. 5, a method of targeting offers 135,according to some embodiments is shown. The method 135 includes 120 and128 previously discussed above. In addition, during 136, the offerserver 102 determines a value of a first metric associated with the userbased on the user information received during 120 in a manner to 122previously discussed above. The value of the first metric may be apostal address, area code, zip code, etc. associated with the user.Thus, in some embodiments, the value of a metric may be a number, textinformation, etc.

[0062] During 137, the offer server 102 verifies the value of the firstmetric determined during 136. In some embodiments of 137, the offerserver 102 may determine if the value of the first metric is consistentwith other information provided in the user information or retrieved orobtained from an information device. Thus, in some embodiments,verification during 137 may be or include a consistency check of userinformation. For example, suppose the user information received during120 includes a street address and a telephone number. If the firstmetric is the street address and the value of the first metric is thespecific street address for the user, during 137 the offer server 102may verify that the specific street address is consistent with the areacode in the telephone number provided in the user information. That is,does the street address provided in the user information fall within thearea code provided in the user information. Thus, the offer server 102can verify the value of the first metric by comparing it against otheror known information (e.g., a known area code that corresponds to thefirst metric) and the area code provided as part of the telephone numberin the user information. In some embodiments, the offer server 102 maystore the known information, retrieve or receive it from an informationdevice, or access an information device to obtain the neededinformation. As another example, the user information obtained in 120also may include a zip code (but not a zip+4 code) for the user, whichmay be the value of the first metric for the user. During 137, the offerserver 102 may verify that the zip code is consistent with the streetaddress (e.g., the street address falls within the zip code) and thetelephone number (e.g., the street address falls within the area code)provided in the user information.

[0063] During 138, the offer server 102 determines a value of a secondmetric from the value of the first metric. For example, if the offerserver 102 knows or can determine the user's street address and/or theuser's zip code (i.e., a value of a first metric), the offer server 102may determine a zip+4 code for the user (i.e., a value of a secondmetric) as previously described above. In some embodiments, some or allof 138 may be performed in conjunction with or as part of 137. Forexample, this may occur when the value of the second metric is providedas part of the user information obtained during 120 and the consistencyverification is done during 137.

[0064] In some embodiments 138 may be or include determining the valueof a second metric using the value of the first metric and otherinformation. For example, if the value of the first metric is a user'sspecific street address, the offer server 102 may use the street addressalong with other user information obtained in 120 (e.g., a zip code) todetermine a specific zip+4 code (e.g., a value of the second metric) ifthe second metric is the zip+4 code. In some embodiments, if otherinformation is used in addition to the value of the first metric tocreate the value of the second metric, the offer server 102 may obtainsome or all of such information from an information device or othersource.

[0065] During 139, the offer server 102 selects one or more offers basedon the value of the second metric, in a manner similar to 126 previouslydiscussed above. In some embodiments, if more than one offer isselected, the offer server 102 may select one or more primary offersand/or one or more secondary offers. The primary offer may be based onthe value of the second metric plus additional information in someembodiments. The secondary offer may be based on the value of the firstmetric, the value of the second metric, other user information, offerselection rules, advertiser criteria, impression minimum or maximumrequirements, etc.

[0066] During 128, the offer server 102 provides data indicative of theselected offer(s) as previously discussed above.

[0067] In some embodiments, the method 135 may include correcting someor all of the user information received during 120, selecting one ormore offers from an offer class, creating a scoring table associatedwith one or more offers, receiving offer information from and advertiserdevice and/or a content providing device, receiving user informationfrom an information device, generating one or more scoring tables ormodels, and/or other variations discussed above with regard to themethod 118.

[0068] Referring now to FIG. 6, a method of targeting offers 140according to some embodiments is shown. At 142 the offer server 102receives a first signal containing data indicative of informationassociated with a user. Such information may be in accordance withembodiments described herein. Selection of a plurality of offers fordisplay on a web page is accomplished at 144. The selection may,according to some embodiments, be based on the data received at 142. Theoffer server 102 may, for example, select multiple scored offers basedon user information and/or metric values associated with a particularuser. Verification of the validity of the user information and/or metricvalues may occur in some embodiments. The offers may be selected inaccordance with scoring models and procedures described herein, or byany other means known or available. Selected offers may be chosen basedon one or more offer classes or types, on an individual offer basis, oron any combination of criteria. In some embodiments, one or morehigh-ranked or scored offers or offer classes may be chosen as primaryoffers 136. Where an offer class is selected based on scoring or otherselection criteria, individual offers within that class may then furtherbe selected by any method, rule, criteria, heuristic, etc. known,available, or described herein.

[0069] In some embodiments, primary offers 136 may be or includetargeted offers that may have high probabilities of being accepted by aparticular user. In some embodiments, primary offers 136 may be offerschosen from a selected offer class. Primary offers 136 may also beselected based on the value of a metric associated with a user, based ona profit score (described elsewhere herein), or based on the number ofimpressions required for a particular offer. An offer impression refersto the display of an offer to a user. Advertisers may require as part oftheir offer campaigns, for example, that a particular offer be served,shown, or displayed a certain number of times and/or to a certain numberof users.

[0070] In some embodiments any number of secondary offers 138 may alsobe selected for simultaneous display on a web page, creating a tieredoffer presentation. The tiered offer presentation is shown in FIG. 4where both primary offers 136 and secondary offers 138 aresimultaneously displayed on a user device 108. In some embodiments,secondary offers 138 may have lesser scores or probabilities of beingaccepted by a user than primary offers 136, or may be selected based onother criteria. For example, secondary offers 138 may be a mix ofvarious offers chosen for display independent of their respective offerscores. These secondary offers 138 may be instead chosen in an attemptto probe a user's interests in areas, subject matter, or genres wheredata on the user is lacking, satisfy one or more rules established byone or more advertisers, meet impression requirements, provide an offertailored to the subject matter of a web site, provide a complementaryoffer, etc. Offer response data acquired from suchstrategically-targeted secondary offers 138 may provide valuable userinformation that may be used to further enhance a scoring model andfuture offer targeting.

[0071] In some embodiments, secondary offers 138 may be chosen fromoffer classes other than a selected offer class. Secondary offers 138may also comprise all offers other than those selected as primary offers136, or may be selected based on a metric associated with a user, basedon a profit score (described elsewhere herein), or based on the numberof impressions required for a particular offer. Thus, secondary offers138 may be targeted or non-targeted offers, and may be chosen based onthe same or different criteria than is used to select primary offers136. In some embodiments, all selected offers are primary offers 136 andno secondary offers 138 are chosen or displayed. Management andcoordination of the selection and/or display of both primary offers 136and secondary offers 138 creates a tiered targeted offer system 100capable of greatly increasing the fulfillment rate of offer servingcampaigns.

[0072] Data indicative of the plurality of selected offers is providedby the offer server 102 at 146. As described herein, the provided datamay include any number of codes, variables, or characters, or may alsobe the selected offers themselves (e.g., graphics, textual material,offer details), as previously described above. In some embodiments thedata is provided to a content providing device 106. In other embodimentsthe data is transmitted and/or displayed directly to a user device 108.In some embodiments, the selected offers are displayed on a web page bythe offer server 102 or content providing device 106.

[0073] An example of a web page residing at a URL 134 and displaying theselected offers is shown in FIG. 4. The URL 134, as described herein,may be associated with or provided by a content providing device 106 orthe offer server 102. As shown in FIG. 4, primary offers 136 andsecondary offers 138 may be displayed simultaneously on a web pagedisplayed on a user device 108. Simultaneous display of offers mayprovide increased probabilities that at least one offer will be acceptedby a user. The exemplary primary offers 136 are displayed in an upperportion of the web page and are presented with associated graphics andin a larger font than the secondary offers 138. Such a layout mayenhance the probability that the already targeted primary offers 136 areaccepted. Secondary offers 138 may be chosen as described herein, or mayalso be a simple listing of all other available offers. Those skilled inthe art will appreciate that any web page layout may be used withoutdeviating from the scope and purpose of the claimed embodiments.

[0074] Each offer shown in FIG. 4 is presented with an associatedcheckbox control that a user may click to select one or more particularoffers. User interaction with the checkbox control and/or other controlsmay indicate an acceptance of an offer or an interest in finding outmore information regarding a given offer or similar offers. Such useraction may direct the user to a web site or page hosted by anadvertising device 104, or a content providing device 108, or may simplyconsummate a transaction between the user and the advertiser or merchantassociated with the chosen offer. User checkbox selection may also causeoffer response data to be sent through a fulfillment path 116 to theoffer server 102. In some embodiments, one or more separate buttonsand/or interactive elements may be used to commit, enter, or submit auser's offer choices. For example, a user may select multipleoffer-related check boxes and then click a “submit” button (not shown)to convey acceptance of the multiple selected offers.

[0075] Turning now to FIG. 7, a block diagram of a system 147 inaccordance with some embodiments is shown. In some situations it may notbe desirable or possible for the offer server 102 to directly serveoffers or offer web pages to a user device 108. Or it may be desirablefor the content providing device 106 to have data regarding potentialoffers. Some embodiments therefore, include offer software 148 thatresides in or on a content providing device 106, and that handles theselection of offers.

[0076] In some embodiments, the offer software 148 may be or include anytype of client-resident software including, but not limited to,extensible markup language (XML) software, or XML-based simple objectaccess protocol (SOAP) software. The offer software 148 is responsiblefor sending data indicative of information associated with a user overan information path 112 to the offer server 102. The offer server 102may verify or otherwise confirm the data and/or a determined metricassociated with a user, and may then transmit the verified (and/orcorrected) information back to the offer software 148 via offer path114. The offer software 148 may include or have access to one or morescoring tables with which it may look-up the verified metric value toretrieve one or more scores for one or more potential offers or offerclasses. In some embodiments, the offer server 102 provides the offersoftware 148 with information and/or data regarding one or moreavailable and/or selected offers. The offer server 102 also may supplyscoring and/or offer targeting information to the offer software 148.The offer software 148 then may select one or more offers, and maydirect, require, or suggest that the content providing engine 150 sendthe selected offer or offers to a user device 108 for display.

[0077] The content providing engine 150 may have, or have access to, theinformation it needs regarding the selected offer(s). The contentproviding engine 150 then can provide the data indicative of theselected offer(s) to the user device 108. The content providing engine150 may be any piece, portion, or component of the content providingdevice 106 separate and/or distinct from the offer software 148. In someembodiments, the offer software 148 may reside within the contentproviding engine 150. In some embodiments, the content providing engine150 and/or the offer software 148 may be implemented in software and/orhardware.

[0078] In some embodiments, the scoring table and/or scoring informationis stored on the offer server 102. Such an arrangement may be desirableto reduce the amount of storage space required by the offer software 148on the content providing device 106. With the scoring table stored onthe offer server 102, the offer software 148 lacks the ability toindependently select offers. Thus, the offer server 102 may not onlyverify and/or correct the data received from the offer software 148, butit may also select one or more offers. Information or data indicative ofwhich offers were selected may then be transmitted to the offer software148 from the offer server 102 via an offer path 114. Similarly, if theoffers are not stored within the content providing device 106, then theoffer server 102 may send data providing the details of the selectedoffer or offers to the offer software 148.

[0079] In some embodiments, the offer software 148 also may beresponsible for sending offer response and/or fulfillment data to theoffer server 102 via a fulfillment path 116. Offer response orfulfillment data may be received or obtained by the offer software 148from the content providing engine 150, directly from a user device 108,from a third-party merchant or seller device, or from any other devicehaving such information.

[0080] Referring now to FIG. 8, a block diagram of a system 100 inaccordance with some embodiments is shown. In some situations it may notbe desirable or possible for the offer server 102 to direct the contentproviding device 106 to serve selected offers. The content providingdevice 106, for example, may not be capable of displaying or servingoffers, or may be prohibited from doing so. In such situations, thecontent providing device 106 may supply the offer server 102 with userinformation via an information path 112. Such information may be dataindicative of information associated with a user as described herein. Insome embodiments, the information includes at least one metricassociated with the user and the user's e-mail address (or informationusable to determine the user's e-mail address) or other contactinformation (e.g., instant messaging address). The offer server 102 maythen select one or more offers in accordance with methods describedherein, and may then send an e-mail to the user via e-mail path 152, thee-mail generally including the selected offer or offers.

[0081] The e-mail sent to the user may be in hyper text markup language(HTML) format or any other format available for sending e-mails. If thee-mail is in HTML format, than a web page containing the selectedoffers, in accordance with some embodiments described herein, may simplybe inserted into the e-mail. Embedded controls may then be interactedwith by the user to generate offer response or offer acceptance data,which may be transmitted to the offer server 102 via a response path154. In some embodiments, the user may directly reply to the e-mail tothe offer server 102 via response path 154. The response may includeoffer acceptance, payment, or inquiry information or requests, and maybe used to both supply advertisers with offer reports and refine andupdate an offer scoring model. If it is not desirable or possible for auser to direct offer response data directly to the offer server 102, thecontent providing device 106 may provide such data via a fulfillmentpath 116. For example, an offer e-mail sent to a user from the offerserver 102 may cause or require the user to communicate with a contentproviding device 106 in order to consummate an offer transaction orreceive more detailed offer information (e.g., the offer e-mail mayinclude a hyperlink to a content providing site). The content providingdevice 106 would then have access to information regarding the user'sactions on the content providing site. This information could then berelayed over the fulfillment path 116 to the offer server 102. Offerserver 102 could then utilize, process, or analyze the response data toimprove consecutive targeting efforts.

[0082] Turning now to FIG. 9, a more detailed description of someembodiments of the targeted offer system 100 and the offer server 102,will be provided. In some embodiments, offer server 102 may include anoffer engine 160. The offer engine 160 may be a PC, a server, aprocessor or processing unit, a program, a procedure, or any otherdevice capable of performing mathematical and/or logical operations.Examples of possible offer engine configurations are described elsewhereherein. In some embodiments, the offer engine 160 performs the selectingof offers and/or the creating and updating of a targeted offer scoringmodel. The offer engine 160 may be connected to a database 162 that maybe used to store a variety of information. The database 162, forexample, may store user information, metric values, reportinginformation, response data, a plurality of offers, and/or one or morescoring tables 164. The scoring table 164 is used to select targetedoffers from the plurality of offers, and is generally created by one ormore scoring models.

[0083] The offer engine 160 may also be connected to an informationserver 166. The information server 166 may be a PC or other processingdevice similar to the possible configurations of the offer engine 160.According to some embodiments, the information server 166 may manage thecommunication of information to and from one or more information devices110. The information server 166 may process or filter information andmay transmit information to the offer engine 160 (e.g., for use inverification, correction, selection, and/or modeling procedures). Theinformation server 166 may also store information directly in thedatabase 162. In some embodiments, the information server 166 may settlepayment and billing issues between the offer server 102 and one or moreinformation devices 110. According to some embodiments, the informationserver 166 may be an information device 110.

[0084] Also connected to the database 162 is a reporting engine 168. Thereporting engine 168 may be a PC, software, or firmware device orcomponent similar to the possible configurations of the offer engine160. The reporting device 168 may utilize data stored in the database162 to provide reports to one or more advertising devices 104. Reportsmay include information on offers selected or served, offer responsedata, billing data, scoring data, and demographic data. The advertisingdevices 104 may themselves be directly connected to the database 162 toenable them to transmit offers to the offer server 102. In someembodiments, the reporting engine 168 may also create reports orreporting data to be used by the offer server 102 in conjunction withoffer targeting analysis or procedures. Various reports may also becreated and disseminated to any other device or combination of devicesinternal or external to the offer targeting system 100.

[0085] The offer engine 160 may also be connected to an e-mail engine170. The e-mail engine 170 may also be any PC, software, or firmwaredevice or component similar to the possible configurations of the offerengine 160. The e-mail engine 170 is used to send targeted offer e-mailsdirectly to one or more user devices 108. In some embodiments, asdescribed herein, the offers selected may be desired or required to besent directly to a user. The e-mail engine 170 may compile, create,design, and/or transmit targeted offer e-mails. In some embodiments, thee-mail engine 170 may also receive e-mail responses from users.Processing and/or analysis of e-mail responses may also be conducted bythe e-mail engine 170.

[0086] Further details of one embodiment of an offer server 102 will nowbe briefly discussed by referring to Figure i 0, where a block diagramof one embodiment of an offer server 102 is shown. As depicted, offerserver 102 is configured as a computing device programmed to operatepursuant to embodiments described herein. In some embodiments, the offerserver 102 may be programmed, designed, or otherwise adapted to executeand/or perform methods for targeting one or more offers as describedherein (i.e., as in conjunction with methods described in relation toFIGS. 3, 5, and 11). As depicted, offer server 102 includes amicroprocessor 200 coupled to a system bus 190 to interact with othercomponents including a communications port 210, one or more inputdevices 220, output devices including a display 230 and a printer 240,and storage devices including RAM 250, ROM 260 and mass storage device270. According to some embodiments, the offer server 102 may includefewer or more components than described in accordance with and shown inFIG. 10, without deviating from the scope and purpose of the claimedembodiments.

[0087] Offer server 102 may communicate with other devices (such asadvertising device 104, content providing device 106, user device 108,and information device 110) via communication port 210 coupled tovarious connections and/or networks. Offer server 102 may be configuredas a Web server adapted to exchange information via the Internet or thelike. According to some embodiments, offer server 102 communicates withother devices via a temporary computer communication channel (e.g., apath through which information can be exchanged). In other words, thecommunication channel between offer server 102 and another device (suchas a user device 108) may be established and discontinued asappropriate. Note that an established communication channel does notneed to be associated with a particular physical path. For example,offer server 102 may exchange information with a content providingdevice 106 via a Web site, in which case packets of information may betransmitted via various physical paths.

[0088] Mass storage device 270 of offer device 102 may store a number ofdifferent programs and data. For example, as shown, offer device 102stores (or has access to) offer selection programs 280, scoring modelprograms 282, web server programs 284, reporting server programs 286,and e-mail server programs 288 which cause offer server 102 to performoffer selection, modeling, web serving, reporting, and e-mail functions.Data stored at (or accessible to) offer server 102 may include data suchas offers 290, offer response data 292 (which may include offerfulfillment data received from one or more content providing devices 106or user devices 108), user information 294, and scoring tables 296(described in more detail herein).

[0089] As illustrated above, in some embodiments, an offer targetingsystem may include a processor, a communications port coupled to theprocessor and adapted to communicate with a plurality of networkdevices, and a storage device coupled to the processor and storinginstructions adapted to be executed by the processor to receive dataindicative of information associated with a user, determine a value of ametric associated with the user based on the data indicative ofinformation associated with the user, verify the value of the metricassociated with the user, select an offer from a plurality of offersbased at least in part on the value of the metric, each of the pluralityof offers having a score associated with the value of the metric, andprovide data indicative of the selected offer. In other embodiments anoffer targeting system may be adapted to generate for each of aplurality of values of a metric, scores associated with a plurality ofoffers, receive fulfillment data on at least one of the plurality ofoffers, and update at least one of the scores based on the fulfillmentdata. In still other embodiments, an offer targeting system may beadapted to receive a first signal, the first signal including dataindicative of information associated with a user, select, based on thedata indicative of information associated with the user, a plurality ofoffers for simultaneous display on a web page, and provide a secondsignal, the second signal including data indicative of the plurality ofoffers. In further embodiments, an offer targeting system may include aprocessor; a communication port coupled to said processor and adapted tocommunicate with a plurality of network devices; and a storage devicecoupled to said processor and storing instructions adapted to beexecuted by said processor to receive data indicative of informationassociated with a user; determine a value of a first metric associatedwith said user based on said data indicative of information associatedwith said user; verify said value of said first metric associated withsaid user; determine a value of a second metric based, at least in parton said value of said first metric; select an offer from a plurality ofoffers based at least in part on said value of said second metric, eachof said plurality of offers having a score associated with said value ofsaid second metric; and provide data indicative of said selected offer.

[0090] Referring now to FIG. 11, a block diagram of an exemplarydatabase table 300 in accordance with some embodiments is shown. Theexemplary database table 300 includes columns for zip+4 codes 302,universal model ranks 304, “bill me” ranks 306, video offersprobabilities 306, and video offers profit scores 310. As shown in FIG.11, various ranks, probabilities, and/or scores are represented for eachzip+4 code value 302. The ranks and scores are values representing theprobabilities of different offer and/or offer types of being accepted byusers from each zip+4 code. More details regarding the scores and themodels used to create them are described below. In accordance withembodiments described herein, other metrics associated with a user(besides and/or in addition to zip+4 codes) may be similarly stored withrespective scores in the same or different tables or databases. Multipletables and/or databases may be used to store the information shown inFIG. 11, and fewer or more database columns and rows may be used inaccordance with some embodiments. In some embodiments, only a singlecolumn representing the scoring values of a single scoring model is usedfor any given metric associated with a user. In other embodiments, aplurality of columns representing scoring models for various individualoffers are used for a single metric. The data shown in FIG. 11 ispresented for exemplary purposes only and those skilled in the art willrecognize that various types and/or configurations of data may be storedin such a table 300 without deviating from the scope of the claimedembodiments.

[0091] Turning now to FIG. 12, a method 320 in accordance with someembodiments is shown. At 322 the offer server 102 and/or a component ofthe offer server 102 such as the offer engine 160 (or themicro-processor 200) generates scores for offers. The offer scores maybe generated using any known or available scoring model including one ormore regression models. Regression models may be processed on one ormore variables, metrics, or values to return statistical and probabilityscores. For example, a regression model may be run on multiple variablesand/or data associated with one or more users to obtain scores or ranksaggregated by zip+4 codes. The regression model may be run on all offerdata in general to develop a universal regression model, or may be runfor specific offers or offer types to develop more specific andspecialized regression models. Results from any one or multipleregression or other scoring models represent the probability of an offeror offer type being accepted or acted upon by a user. For example, usinghistorical offer acceptance data for a soft drink offer, a regressionmodel is run to provide scores for each known zip+4 code. The model is astatistical tool that correlates and/or normalizes the historical dataand aggregates it by zip+4 code. From the correlated and/or normalizeddata, trends can be identified that may indicate certain or severalzip+4 codes where the soft drink offer has often been or is likely to beaccepted. Each zip+4 code is therefore assigned a probability-relatedscore regarding the soft drink offer. The scores may then be used toincrease soft drink offer acceptance rates by targeting soft drinkoffers to those zip+4 codes that are more likely to accept such offers.Those skilled in the art will recognize that regression data may becompiled for any number of variables and/or metrics including zip+4codes, households, area codes, and individual users. In someembodiments, regression models compiled for various metrics may be usedto create one or more “match codes” representing various degrees ofregression model accuracy. For example, a regression model providingscores aggregated by zip+4 codes may be associated with a match codeindicating that the model is highly accurate (i.e., for any given useran appropriate offer is likely to be targeted based on the regressionscores). While a regression model for individual users may have a highermatch code indicating a higher accuracy probability. These match codesrepresent the fact that the scoring model based on individual users islikely to be more accurate than the one based on zip+4 codes (e.g.,members of the same zip+4 may have different interests, so an offertargeted to one such user may not actually be appropriate for a As shownin FIG. 11, the scoring table 300 contains columns for each regressionmodel 304-310 and rows for each known value of zip+4 codes 302. Forexample, a universal model rank 304 is created for each zip+4 code 302by running a zip+4 regression for all offers or offer types. Theresulting scores shown in column 304 represent the probability rank orrelative value of presenting an offer to a user from any given zip+4code 302. Zip+4 code “262618799”, for example, has a universal modelrank 304 of ten. Assuming ranks are assigned based on values from one toten, ten being the highest, zip+4 code “262618799” is a top-rankingzip+4 code 302. Offers served to a user from this zip+4 code 302 wouldtherefore have a high probability of being accepted or acted upon. Incomparison, zip+4 code “068420018” has a rank of two. Offers served tousers from “068420018” would therefore have a much lower likelihood ofbeing accepted then those served to “262618799” associated users. Thoseskilled in the art will realize that any available ranking scheme willcomply with the objectives of present embodiments.

[0092] In some embodiments, the offer server 102 and/or one of itscomponents may select an offer based on such universal model ranks 304.In such embodiments, only users from zip+4 codes ranking above a certainthreshold level will be shown offers. In this way the probability ofhaving a served offer accepted is increased. Offers may also be selectedbased on other regression models 306-310 and/or an average orcombination of regression models 304-310. For example, column 306 showsregression ranks for zip+4 codes based on an offer class-specificregression model. The regression model is run for only “bill me”-typeoffers. The resulting ranks 306 indicate the relative probability of a“bill me” offer being accepted in the various zip+4 codes 302. Thevarious zip+4 codes 302 may have identical rank scores for the differentranking models 304-306 (as shown for zip+4 code “262618799”), or theranks may differ from model to model (as shown for all other representedzip+4 codes 302).

[0093] Offer selection may be based solely on either the universal modelrank 304 or the “bill me” rank 306. Offers may also be selected based onan average of the two model ranks 304, 306 or may be selected using anyother form of mathematical or logical processing and selection. In someembodiments, “bill me” offers are selected based on the “bill me” ranks306, while other offers may be selected based on the universal modelranks 304. As the regression model is made more specific, theprobability that a served offer will be accepted generally increases.Thus, “bill me” offers served using “bill me” regression ranks 306 willbe more likely to be accepted than any type of offer served using auniversal model rank 304.

[0094] Use of a more targeted regression model will thus generallyincrease the effectiveness of an offer serving campaign. For example,zip+4 code “210320291” has a universal model rank 304 of five and a“bill me” rank 306 of six. If a business decision is made to only serveads to zip+4 codes 302 with ranks higher than five, than users fromzip+4 code “210320291” will not receive offers under the universalregression model, but will under the “bill me” regression model. If theoffer to be served is a “bill me” offer, then users from the zip+4 code“210320291” would qualify for the offer when the more specific model isapplied. Thus, the more specific model allows offers to be targeted tousers that have a good probability of accepting a specific offer type,but who would normally not be served offers in the absence of the morespecific model. Similarly, some zip+4 codes 302 (like “068400001”) maybe served an offer under the universal model when they do not qualifyunder the more specific model. Manipulation and/or management of thevarious model scores and models allow the offer server 102 to greatlyincrease the effectiveness of each offer to be served.

[0095] Other scoring models may provide actual probabilities of an offerbeing accepted in a given zip+4 code 302. For example, the video offersprobabilities 308 indicate that certain zip+4 codes 302 (like“262618799” with a 93% chance) are much more likely to acceptvideo-related offers than others (like “001835574” with a 4% chance).Offers may be selected in similar fashion as that described for othermodels herein. In some embodiments, probability scores allow anadvertiser or merchant (or the offer server 102) to choose a specificprobability percentage point as a threshold. Simple reports may providepercentage point break-downs of revenue realized, revenue missed, ornumber or percentage of users targeted. Threshold levels may then bechosen to increase offer exposure, revenue, or acceptance rates whiledecreasing (in relative terms) marketing expenditures.

[0096] According to some embodiments, one or more offers may be selectedbased on a profit score. Although a profit score may be defined inseveral ways, one representative example will be described herein toclarify the general method of determining such a value. For example, aprofit score may simply be the probability of an offer being acceptedmultiplied by the revenue associated with that given offer. For example,the video probability rank of 93% for zip+4 “262618799” in FIG. 11multiplied by an offer revenue of $0.72 (not shown), for example, yieldsa video profit score 310 of $0.67. Offers may therefore be selectedbased on a probability-weighted revenue value, or profit score, toincrease the amount of revenue obtained through offer targeting.

[0097] The revenue component of a profit score may represent severaldifferent values. In some embodiments, the offer revenue may representthe amount of revenue expected by an advertiser or merchant upon offeracceptance (e.g., a margin or profit). The offer revenue may alsoindicate the amount of money an advertiser or merchant is willing to payto have an offer served. Thus, the profit score may represent a revenueoptimization from the perspectives of various involved parties includingoperators of the offer server 102 and of advertising devices 104. Offersmay also be selected based on any number of other criteria, scores, orrankings related to, for example, advertiser campaign goals, offerserver 102 goals, and/or content provider policies, goals, orobjectives. Those skilled in the art will also appreciate that scoringmodels may be run for multiple offers (or even every available offer) toprovide an offer-specific score for each metric value. In someembodiments, one or more offers may be selected based on the type ofuser device 108. For example, different offers may be selected for usersof wireless telephone user devices 108 than for personal digitalassistant (PDA) user devices 108.

[0098] Returning now to FIG. 12, fulfillment data is received at 324 bythe offer server 102. The data may be received according to methodsdescribed herein, or may arrive by any other manner from any number ofpossible data sources. In some embodiments, the fulfillment datapertains to one or more of the offers that were scored and/or ranked at322. The fulfillment data may contain data regarding accepted offers anddata regarding any other actions or inactions associated with an offer,offers, or offer type or types. The fulfillment data is then used at 326to update the scoring model and the respective offer scores. In thismanner, the targeted offer scores are intermittently or continuallyupdated to reflect the most current probabilities based on the mostcurrent offer response and/or fulfillment data. As described herein,such updating may be accomplished in real-time to enable each systemuser to be targeted with a timely and appropriate offer. In someembodiments, fulfillment data may be received from multiple sources suchas from several content providing devices 106. Such data collected frommany sources may be utilized to create and/or update one or more scoringmodels for targeting offers. Multi-source scoring models may be moreaccurate, in some embodiments, because they take into account data fromvarious sources. Generally, the accuracy of a model increases as theamount of data and the amount of data sources used increases.

[0099] The methods of the present invention may be embodied as acomputer program developed using an object oriented language that allowsthe modeling of complex systems with modular objects to createabstractions that are representative of real world, physical objects andtheir interrelationships. However, it would be understood by one ofordinary skill in the art that the invention as described herein couldbe implemented in many different ways using a wide range of programmingtechniques as well as general-purpose hardware systems or dedicatedcontrollers. In addition, many, if not all, of the elements for themethods described above are optional or can be combined or performed inone or more alternative orders or sequences without departing from thescope of the present invention and the claims should not be construed asbeing limited to any particular order or sequence, unless specificallyindicated.

[0100] Each of the methods described above can be performed on a singlecomputer, computer system, microprocessor, etc. In addition, in someembodiments, two or more of the elements in each of the methodsdescribed above could be performed on two or more different computers,computer systems, microprocessors, etc., some or all of which may belocally or remotely configured. The methods can be implemented in anysort or implementation of computer software, program, sets ofinstructions, programming means, code, ASIC, or specially designedchips, logic gates, or other hardware structured to directly effect orimplement such software, programs, sets of instructions, programmingmeans, or code. The computer software, program, sets of instructions orcode can be storable, writeable, or savable on any computer usable orreadable media or other program storage device or media such as a floppyor other magnetic or optical disk, magnetic or optical tape, CD-ROM,DVD, punch cards, paper tape, hard disk drive, Zip™ disk, flash oroptical memory card, microprocessor, solid state memory device, RAM,EPROM, or ROM.

[0101] Although the present invention has been described with respect tovarious embodiments thereof, those skilled in the art will note thatvarious substitutions may be made to those embodiments described hereinwithout departing from the spirit and scope of the present invention.The invention described in the above detailed description is notintended to be limited to the specific form set forth herein, but isintended to cover such alternatives, modifications and equivalents ascan reasonably be included within the spirit and scope of the appendedclaims.

[0102] The words “comprise,” “comprises,” “comprising,” “include,”“including,” and “includes” when used in this specification and in thefollowing claims are intended to specify the presence of statedfeatures, elements, integers, components, or steps, but they do notpreclude the presence or addition of one or more other features,elements, integers, components, steps, or groups thereof.

What is claimed is:
 1. A method, comprising: receiving data indicativeof information associated with a user; determining a value of a metricassociated with said user based on said data indicative of informationassociated with said user; verifying said value of said metricassociated with said user; selecting an offer from a plurality of offersbased at least in part on said value of said metric, each of saidplurality of offers having a score associated with said value of saidmetric; and providing data indicative of said selected offer.
 2. Amethod according to claim 1, further comprising: receiving response dataassociated with said selected offer.
 3. A method according to claim 2,further comprising: generating a report on said selected offer, saidreport including said response data.
 4. A method according to claim 1,further comprising: generating scores for a plurality of offers based onsaid value of said metric associated with said user.
 5. A methodaccording to claim 4, further comprising: creating at least one table ofscores for said plurality of offers for a plurality of said values ofsaid metric associated with said user.
 6. A method according to claim 4,further comprising: updating said scores based on offer response data.7. A method according to claim 1, further comprising: receiving saiddata indicative of said selected offer.
 8. A method according to claim7, wherein said receiving said data indicative of said selected offerincludes receiving said data indicative of said selected offer from acontent providing device.
 9. A method according to claim 7, wherein saidreceiving said data indicative of said selected offer includes receivingsaid data indicative of said selected offer from a content advertisingdevice.
 10. A method according to claim 1, wherein said receiving saiddata indicative of information associated with said user includesreceiving said data indicative of information associated with said userfrom a content providing device.
 11. A method according to claim 1,wherein said receiving said data indicative of information associatedwith said user includes receiving said data indicative of informationassociated with said user from a user device.
 12. A method according toclaim 1, wherein said determining said value of said metric includes:determining a zip+4 code associated with said user based on said dataindicative of information associated with said user.
 13. A methodaccording to claim 1, wherein verifying that said value of said metricassociated with said user is valid includes: comparing said value ofsaid metric associated with said user to at least one known value ofsaid metric.
 14. A method according to claim 1, wherein verifying thatsaid value of said metric associated with said user is valid includes:receiving information associated with said value of said metric from aninformation device.
 15. A method according to claim 1, wherein verifyingsaid value of said metric associated with said user includes: correctingsaid value of said metric associated with said user.
 16. A methodaccording to claim 1, wherein said selecting an offer from saidplurality of offers includes: selecting at least one offer type based atleast in part on a score of said at least one offer type associated withsaid value of said metric.
 17. A method according to claim 16, whereinsaid selecting an offer from said plurality of offers includes:selecting at least one offer from said selected at least one offer type.18. A method according to claim 1, wherein said selecting an offer fromsaid plurality of offers includes: selecting multiple offers from saidplurality of offers.
 19. A method according to claim 18, wherein saidmultiple offers are selected for simultaneous display on a web page. 20.A method according to claim 18, wherein said selecting multiple offersfrom said plurality of offers includes: selecting at least one primaryoffer; and selecting at least one secondary offer.
 21. A methodaccording to claim 20, wherein said at least one primary offer isselected based at least in part on said score associated with said valueof said metric.
 22. A method according to claim 21, wherein said atleast one secondary offer is selected based at least in part on saidscore associated with said value of said metric.
 23. A method accordingto claim 1, wherein said providing data indicative of said selectedoffer includes: providing data indicative of said selected offer to atleast one content providing device.
 24. A method according to claim 1,wherein said providing data indicative of said selected offer includes:providing data indicative of said selected offer to at least one userdevice.
 25. A method according to claim 1, wherein said data indicativeof said selected offer includes an offer code associated with saidselected offer.
 26. A method according to claim 1, wherein said metricis a first metric and said verifying of said value of said metricassociated with said user includes: determining at least one value of asecond metric associated with said user; and determining a correct valueof said first metric based at least in part on said value of said secondmetric.
 27. A method according to claim 1, wherein said score is aprofit score.
 28. A method according to claim 1, wherein said providingsaid data indicative of said selected offer includes: providing saiddata indicative of said selected offer on a web page.
 29. A methodaccording to claim 1, wherein providing said data indicative of saidselected offer includes: providing said data indicative of said selectedoffer in an e-mail message.
 30. An article of manufacture comprising: acomputer readable medium having stored thereon instructions which, whenexecuted by a processor, cause said processor to: receive dataindicative of information associated with a user; determine a value of ametric associated with said user based on said data indicative ofinformation associated with said user; verify said value of said metricassociated with said user; select an offer from a plurality of offersbased at least in part on said value of said metric, each of saidplurality offers having a score associated with said value of saidmetric; and provide data indicative of said selected offer.
 31. Anarticle of manufacture according to claim 30, wherein said storedinstructions, when executed by a processor, further cause said processorto: receive response data associated with said selected offer.
 32. Anarticle of manufacture according to claim 30, wherein said storedinstructions, when executed by a processor, further cause said processorto: generate scores for a plurality of offers based on said value ofsaid metric associated with said user.
 33. An article of manufactureaccording to claim 30, wherein said offer is selected from saidplurality of offers by: selecting at least one offer type based at leastin part on a score of said at least one offer type associated with saidvalue of said metric.
 34. An article of manufacture according to claim30, wherein said offer is selected from said plurality of offers by:selecting multiple offers from said plurality of offers.
 35. An articleof manufacture according to claim 34, wherein said selecting multipleoffers from said plurality of offers includes: selecting at least oneprimary offer; and selecting at least one secondary offer.
 36. An offertargeting system, comprising: a processor; a communication port coupledto said processor and adapted to communicate with a plurality of networkdevices; and a storage device coupled to said processor and storinginstructions adapted to be executed by said processor to: receive dataindicative of information associated with a user; determine a value of ametric associated with said user based on said data indicative ofinformation associated with said user; verify said value of said metricassociated with said user; select an offer from a plurality of offersbased at least in part on said value of said metric, each of saidplurality of offers having a score associated with said value of saidmetric; and provide data indicative of said selected offer.
 37. Amethod, comprising: generating for each of a plurality of values of ametric, scores associated with a plurality of offers; receivingfulfillment data associated with at least one of said plurality ofoffers; and updating at least one of said scores based on saidfulfillment data.
 38. A method according to claim 37, wherein each ofsaid scores is a profit score.
 39. An article of manufacture comprising:a computer readable medium having stored thereon instructions which,when executed by a processor, cause said processor to: generate for eachof a plurality of values of a metric, scores associated with a pluralityof offers; receive fulfillment data associated with at least one of saidoffers; and update at least one of said scores based on said fulfillmentdata.
 40. An offer targeting system, comprising: a processor; acommunication port coupled to said processor and adapted to communicatewith a plurality of network devices; and a storage device coupled tosaid processor and storing instructions adapted to be executed by saidprocessor to: generate for each of a plurality of values of a metric,scores associated with a plurality of offers; receive fulfillment dataon at least one of said plurality of offers; and update at least one ofsaid scores based on said fulfillment data.
 41. A method, comprising:receiving a first signal, said first signal including data indicative ofinformation associated with a user; selecting, based on said dataindicative of information associated with said user, a plurality ofoffers for simultaneous display on a web page provided to said user; andproviding a second signal, said second signal including data indicativeof said plurality of offers.
 42. A method according to claim 41, saidselecting comprising: determining, based on said data indicative ofinformation associated with said user, a class of offers; and selectinga plurality of offers from said class.
 43. A method according to claim41, said further comprising: verifying at least a portion of said dataindicative of information associated with said user.
 44. A methodaccording to claim 41, said selecting comprising: selecting at least oneprimary offer and at least one secondary offer.
 45. A method accordingto claim 44, wherein said at least one primary offer is selected basedon a first portion of said data indicative of information associatedwith said user, and wherein said at least one secondary offer isselected based on a second portion of said data indicative ofinformation associated with said user.
 46. An article of manufacturecomprising: a computer readable medium having stored thereoninstructions which, when executed by a processor, cause said processorto: receive a first signal, said first signal including data indicativeof information associated with a user; select, based on said dataindicative of information associated with said user, a plurality ofoffers for simultaneous display on a web page; and provide a secondsignal, said second signal including data indicative of said pluralityof offers.
 47. An article of manufacture according to claim 46, whereinsaid stored instructions, when executed by a processor, further causesaid processor to: verify at least a portion of said data indicative ofinformation associated with said user.
 48. An article of manufactureaccording to claim 46, wherein said plurality of offers are selected by:selecting at least one primary offer and at least one secondary offer.49. An offer targeting system, comprising: a processor; a communicationport coupled to said processor and adapted to communicate with aplurality of network devices; and a storage device coupled to saidprocessor and storing instructions adapted to be executed by saidprocessor to: receive a first signal, said first signal including dataindicative of information associated with a user; select, based on saiddata indicative of information associated with said user, a plurality ofoffers for simultaneous display on a web page; and provide a secondsignal, said second signal including data indicative of said pluralityof offers.
 50. A system according to claim 49, wherein said storedinstructions are further adapted to be executed by said processor to:verify at least a portion of said data indicative of informationassociated with said user.
 51. A system according to claim 49, whereinsaid plurality of offers are selected by: selecting at least one primaryoffer and at least one secondary offer.
 52. A method, comprising:receiving data indicative of information associated with a user;determining a value of a first metric associated with said user based onsaid data indicative of information associated with said user; verifyingsaid value of said first metric associated with said user; determining avalue of a second metric based, at least in part, on said value of saidfirst metric; selecting an offer from a plurality of offers based atleast in part on said value of said second metric, each of saidplurality of offers having a score associated with said value of saidsecond metric; and providing data indicative of said selected offer. 53.A method according to claim 52, further comprising: receiving responsedata associated with said selected offer.
 54. A method according toclaim 52, further comprising: generating scores for a plurality ofoffers based on said value of said second metric associated with saiduser.
 55. A method according to claim 54, further comprising: creatingat least one table of scores for said plurality of offers for aplurality of said values of said second metric associated with saiduser.
 56. A method according to claim 55, further comprising: updatingsaid scores based on offer response data.
 57. A method according toclaim 52, wherein said determining said value of said second metricincludes: determining a zip+4 code associated with said user based onsaid value of said first metric.
 58. A method according to claim 52,wherein said selecting an offer from said plurality of offers includes:selecting multiple offers from said plurality of offers.
 59. A methodaccording to claim 58, wherein said selecting multiple offers from saidplurality of offers includes: selecting at least one primary offer; andselecting at least one secondary offer.
 60. An article of manufacturecomprising: a computer readable medium having stored thereoninstructions which, when executed by a processor, cause said processorto: receive data indicative of information associated with a user;determine a value of a first metric associated with said user based onsaid data indicative of information associated with said user; verifysaid value of said first metric associated with said user; determine avalue of a second metric based, at least in part, on said value of saidfirst metric; select an offer from a plurality of offers based at leastin part on said value of said second metric, each of said plurality ofoffers having a score associated with said value of said second metric;and provide data indicative of said selected offer.
 61. An offertargeting system, comprising: a processor; a communication port coupledto said processor and adapted to communicate with a plurality of networkdevices; and a storage device coupled to said processor and storinginstructions adapted to be executed by said processor to: receive dataindicative of information associated with a user; determine a value of afirst metric associated with said user based on said data indicative ofinformation associated with said user; verify said value of said firstmetric associated with said user; determine a value of a second metricbased, at least in part, on said value of said first metric; select anoffer from a plurality of offers based at least in part on said value ofsaid second metric, each of said plurality of offers having a scoreassociated with said value of said second metric; and provide dataindicative of said selected offer.